import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class        _snake_case    (    a_					):
					lowerCamelCase__:		Dict	=  ["image_processor", "tokenizer"]
					lowerCamelCase__:		Union[str, Any]	=  "AutoImageProcessor"
					lowerCamelCase__:		Optional[int]	=  "AutoTokenizer"
					def __init__(	self:					Optional[int]						,	__lowerCamelCase:					List[Any]=None						,	__lowerCamelCase:					Tuple=None						,	**__lowerCamelCase:					int  )     ->					int:
												__UpperCAmelCase    :      List[Any]       = None
												if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."						,	UpperCamelCase_						,	)
																			__UpperCAmelCase    :      int       = kwargs.pop("feature_extractor"  )

												__UpperCAmelCase    :      int       = image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."  )
												if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."  )

												super().__init__(UpperCamelCase_						,	UpperCamelCase_  )
												__UpperCAmelCase    :      Optional[int]       = self.image_processor
												__UpperCAmelCase    :      Any       = False
					def __call__(	self:					List[str]						,	*__lowerCamelCase:					str						,	**__lowerCamelCase:					str  )     ->					List[str]:

												if self._in_target_context_manager:
																			return self.current_processor(*UpperCamelCase_						,	**UpperCamelCase_  )

												__UpperCAmelCase    :      Tuple       = kwargs.pop("images"						,	UpperCamelCase_  )
												__UpperCAmelCase    :      Any       = kwargs.pop("text"						,	UpperCamelCase_  )
												if len(UpperCamelCase_  ) > 0:
																			__UpperCAmelCase    :      List[str]       = args[0]
																			__UpperCAmelCase    :      int       = args[1:]

												if images is None and text is None:
																			raise ValueError("You need to specify either an `images` or `text` input to process."  )

												if images is not None:
																			__UpperCAmelCase    :      Dict       = self.image_processor(UpperCamelCase_						,	*UpperCamelCase_						,	**UpperCamelCase_  )
												if text is not None:
																			__UpperCAmelCase    :      str       = self.tokenizer(UpperCamelCase_						,	**UpperCamelCase_  )

												if text is None:
																			return inputs
												elif images is None:
																			return encodings
												else:
																			__UpperCAmelCase    :      int       = encodings["input_ids"]
																			return inputs
					def  _lowerCamelCase			(	self:					str						,	*__lowerCamelCase:					Dict						,	**__lowerCamelCase:					str  )     ->					Dict:
												return self.tokenizer.batch_decode(*UpperCamelCase_						,	**UpperCamelCase_  )
					def  _lowerCamelCase			(	self:					Any						,	*__lowerCamelCase:					str						,	**__lowerCamelCase:					int  )     ->					Optional[Any]:
												return self.tokenizer.decode(*UpperCamelCase_						,	**UpperCamelCase_  )
					@contextmanager
					def  _lowerCamelCase			(	self:					int  )     ->					int:
												warnings.warn(
												    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
												    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
												    "your images inputs, or in a separate call."  )
												__UpperCAmelCase    :      List[str]       = True
												__UpperCAmelCase    :      Any       = self.tokenizer
												yield
												__UpperCAmelCase    :      Tuple       = self.image_processor
												__UpperCAmelCase    :      Union[str, Any]       = False
					def  _lowerCamelCase			(	self:					List[str]						,	__lowerCamelCase:					Dict						,	__lowerCamelCase:					Optional[Any]=False						,	__lowerCamelCase:					Optional[int]=None  )     ->					List[Any]:

												if added_vocab is None:
																			__UpperCAmelCase    :      List[Any]       = self.tokenizer.get_added_vocab()

												__UpperCAmelCase    :      Any       = {}

												while tokens:
																			__UpperCAmelCase    :      List[Any]       = re.search(R"<s_(.*?)>"						,	UpperCamelCase_						,	re.IGNORECASE  )
																			if start_token is None:
																										break
																			__UpperCAmelCase    :      int       = start_token.group(1  )
																			__UpperCAmelCase    :      Any       = re.search(Rf'''</s_{key}>'''						,	UpperCamelCase_						,	re.IGNORECASE  )
																			__UpperCAmelCase    :      Dict       = start_token.group()
																			if end_token is None:
																										__UpperCAmelCase    :      Optional[Any]       = tokens.replace(UpperCamelCase_						,	""  )
																			else:
																										__UpperCAmelCase    :      Optional[int]       = end_token.group()
																										__UpperCAmelCase    :      Tuple       = re.escape(UpperCamelCase_  )
																										__UpperCAmelCase    :      Dict       = re.escape(UpperCamelCase_  )
																										__UpperCAmelCase    :      Tuple       = re.search(f'''{start_token_escaped}(.*?){end_token_escaped}'''						,	UpperCamelCase_						,	re.IGNORECASE  )
																										if content is not None:
																																	__UpperCAmelCase    :      Optional[Any]       = content.group(1  ).strip()
																																	if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																								__UpperCAmelCase    :      Optional[int]       = self.tokenajson(UpperCamelCase_						,	is_inner_value=UpperCamelCase_						,	added_vocab=UpperCamelCase_  )
																																								if value:
																																															if len(UpperCamelCase_  ) == 1:
																																																						__UpperCAmelCase    :      List[Any]       = value[0]
																																															__UpperCAmelCase    :      Dict       = value
																																	else:  # leaf nodes
																																								__UpperCAmelCase    :      List[Any]       = []
																																								for leaf in content.split(R"<sep/>"  ):
																																															__UpperCAmelCase    :      Dict       = leaf.strip()
																																															if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																						__UpperCAmelCase    :      int       = leaf[1:-2]  # for categorical special tokens
																																															output[key].append(UpperCamelCase_  )
																																								if len(output[key]  ) == 1:
																																															__UpperCAmelCase    :      Tuple       = output[key][0]

																										__UpperCAmelCase    :      Union[str, Any]       = tokens[tokens.find(UpperCamelCase_  ) + len(UpperCamelCase_  ) :].strip()
																										if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																	return [output] + self.tokenajson(tokens[6:]						,	is_inner_value=UpperCamelCase_						,	added_vocab=UpperCamelCase_  )

												if len(UpperCamelCase_  ):
																			return [output] if is_inner_value else output
												else:
																			return [] if is_inner_value else {"text_sequence": tokens}
					@property
					def  _lowerCamelCase			(	self:					Optional[int]  )     ->					str:
												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,	UpperCamelCase_						,	)
												return self.image_processor_class





					@property
					def  _lowerCamelCase			(	self:					List[Any]  )     ->					Any:
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,	UpperCamelCase_						,	)
												return self.image_processor












"""simple docstring"""
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							A						(				__snake_case: Tuple  )    ->		Optional[Any]:

       """simple docstring"""





       if (
           (cp >= 0x4_E00 and cp <= 0x9_FFF)
           or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
           or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
           or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
           or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
           or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
           or (cp >= 0xF_900 and cp <= 0xF_AFF)
           or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
       ):  #
              return True

       return False


def 							A						(				__snake_case: str  )    ->		Optional[Any]:

       """simple docstring"""





       for char in word:
              __magic_name__      				=    ord(__snake_case  )
              if not _is_chinese_char(__snake_case  ):
                     return 0
       return 1


def 							A						(				__snake_case: List[str]  )    ->		List[str]:

       """simple docstring"""





       __magic_name__      				=    set()

       for token in tokens:
              __magic_name__      				=    len(__snake_case  ) > 1 and is_chinese(__snake_case  )
              if chinese_word:
                     word_set.add(__snake_case  )
       __magic_name__      				=    list(__snake_case  )
       return word_list


def 							A						(				__snake_case: List[str] ,     __snake_case: set()  )    ->		str:

       """simple docstring"""





       if not chinese_word_set:
              return bert_tokens
       __magic_name__      				=    max([len(__snake_case  ) for w in chinese_word_set]  )

       __magic_name__      				=    bert_tokens
       __magic_name__				,  __magic_name__      				=    0, len(__snake_case  )
       while start < end:
              __magic_name__      				=    True
              if is_chinese(bert_word[start]  ):
                     __magic_name__      				=    min(end - start ,     __snake_case  )
                     for i in range(__snake_case ,     1 ,     -1  ):
                            __magic_name__      				=    ''.join(bert_word[start : start + i]  )
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1 ,     start + i  ):
                                          __magic_name__      				=    '##' + bert_word[j]
                                   __magic_name__      				=    start + i
                                   __magic_name__      				=    False
                                   break
              if single_word:
                     start += 1
       return bert_word


def 							A						(				__snake_case: List[str] ,     __snake_case: LTP ,     __snake_case: BertTokenizer  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    []

       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,     tasks=['cws']  ).cws
              __magic_name__      				=    [get_chinese_word(__snake_case  ) for r in res]
              ltp_res.extend(__snake_case  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for i in range(0 ,     len(__snake_case  ) ,     1_0_0  ):
              __magic_name__      				=    bert_tokenizer(lines[i : i + 1_0_0] ,     add_special_tokens=__snake_case ,     truncation=__snake_case ,     max_length=5_1_2  )
              bert_res.extend(res['input_ids']  )
       assert len(__snake_case  ) == len(__snake_case  )

       __magic_name__      				=    []
       for input_ids, chinese_word in zip(__snake_case ,     __snake_case  ):
              __magic_name__      				=    []
              for id in input_ids:
                     __magic_name__      				=    bert_tokenizer._convert_id_to_token(__snake_case  )
                     input_tokens.append(__snake_case  )
              __magic_name__      				=    add_sub_symbol(__snake_case ,     __snake_case  )
              __magic_name__      				=    []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(__snake_case  ):
                     if token[:2] == "##":
                            __magic_name__      				=    token[2:]
                            # save chinese tokens' pos
                            if len(__snake_case  ) == 1 and _is_chinese_char(ord(__snake_case  )  ):
                                   ref_id.append(__snake_case  )
              ref_ids.append(__snake_case  )

       assert len(__snake_case  ) == len(__snake_case  )

       return ref_ids


def 							A						(				__snake_case: Optional[Any]  )    ->		List[str]:

       """simple docstring"""





       with open(args.file_name ,     'r' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    f.readlines()
       __magic_name__      				=    [line.strip() for line in data if len(__snake_case  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       __magic_name__      				=    LTP(args.ltp  )  # faster in GPU device
       __magic_name__      				=    BertTokenizer.from_pretrained(args.bert  )

       __magic_name__      				=    prepare_ref(__snake_case ,     __snake_case ,     __snake_case  )

       with open(args.save_path ,     'w' ,     encoding='utf-8'  ) as f:
              __magic_name__      				=    [json.dumps(__snake_case  ) + '\n' for ref in ref_ids]
              f.writelines(__snake_case  )


if __name__ == "__main__":
 snake_case : Tuple     		=		argparse.ArgumentParser(description="""prepare_chinese_ref""")
 parser.add_argument(
     """--file_name""",
     required=False,
     type=str,
     default="""./resources/chinese-demo.txt""",
     help="""file need process, same as training data in lm""",
 )
 parser.add_argument(
     """--ltp""",
     required=False,
     type=str,
     default="""./resources/ltp""",
     help="""resources for LTP tokenizer, usually a path""",
 )
 parser.add_argument(
     """--bert""",
     required=False,
     type=str,
     default="""./resources/robert""",
     help="""resources for Bert tokenizer""",
 )
 parser.add_argument(
     """--save_path""",
     required=False,
     type=str,
     default="""./resources/ref.txt""",
     help="""path to save res""",
 )

 snake_case : Any     		=		parser.parse_args()
 main(args)